CN112884319B

CN112884319B - Task allocation method and device, computer equipment and storage medium

Info

Publication number: CN112884319B
Application number: CN202110186453.8A
Authority: CN
Inventors: 荣灿; 李勇; 郭殿升; 孙福宁
Original assignee: Tsinghua University; Tencent Dadi Tongtu Beijing Technology Co Ltd
Current assignee: Tsinghua University; Tencent Dadi Tongtu Beijing Technology Co Ltd
Priority date: 2021-02-10
Filing date: 2021-02-10
Publication date: 2023-11-03
Anticipated expiration: 2041-02-10
Also published as: CN112884319A

Abstract

The embodiment of the application discloses a task allocation method, a device, computer equipment and a storage medium, wherein the task allocation method can be applied to the field of electronic maps, and the method comprises the following steps: acquiring a plurality of tasks for collecting map information, and clustering the plurality of tasks according to task characteristics of each task to obtain a plurality of task clusters; determining a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in a plurality of task clusters, wherein the target movement rule diagram is used for indicating path distribution of the target user in N first areas in map information, and the task distribution rule diagram is used for indicating task distribution in N second areas in the map information; determining a target task cluster corresponding to a target user from a plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster; and distributing the target task cluster to the target user so that the target user executes each task in the target task cluster, thereby being beneficial to improving the efficiency of task acquisition.

Description

Task allocation method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a task allocation method, a task allocation device, a computer device, and a storage medium.

Background

With the rapid development of internet technology, electronic maps are also applied to people in daily life, and with the increase of use demands, users have higher and higher requirements on timeliness and accuracy of information provided by the electronic maps.

However, the buildings and the layout in the city are also changing increasingly, so that to meet the requirements of users on timeliness and accuracy of the electronic map, the city change needs to be collected in time, and the information collection efficiency of the electronic map is improved. Therefore, how to reasonably collect the electronic map information so as to improve the information collection efficiency becomes a research hotspot of current information collection.

Disclosure of Invention

The embodiment of the application provides a task allocation method, a device, computer equipment and a storage medium, which can reasonably allocate tasks to corresponding users by combining the space-time characteristics of map information acquisition tasks and the movement rules of the users, and can effectively improve the acquisition efficiency.

The first aspect of the embodiment of the application discloses a task allocation method, which comprises the following steps:

Acquiring a plurality of tasks for collecting map information, and clustering the plurality of tasks according to task characteristics of each task to obtain a plurality of task clusters;

determining a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, wherein the target movement rule diagram is used for indicating path distribution of the target user in N first areas in the map information, and the task distribution rule diagram is used for indicating task distribution in N second areas in the map information, and N is a positive integer;

determining a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster;

and distributing the target task cluster to the target user so that the target user executes each task in the target task cluster.

The second aspect of the embodiment of the application discloses a task allocation device, which comprises:

the clustering unit is used for acquiring a plurality of tasks for collecting map information, and clustering the tasks according to task characteristics of each task to obtain a plurality of task clusters;

A first determining unit, configured to determine a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, where the target movement rule diagram is used to indicate path distribution of the target user in N first areas in the map information, and the task distribution rule diagram is used to indicate task distribution in the N second areas in the map information, where N is a positive integer;

the second determining unit is used for determining a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster;

and the allocation unit is used for allocating the target task cluster to the target user so that the target user executes each task in the target task cluster.

A third aspect of the embodiments of the present application discloses a computer device, comprising a processor, a memory and a network interface, the processor, the memory and the network interface being connected to each other, wherein the memory is configured to store a computer program, the computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of the first aspect.

A fourth aspect of the embodiments of the present application discloses a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of the first aspect described above.

A fifth aspect of the embodiments of the present application discloses a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from a computer-readable storage medium by a processor of a computer device, the computer instructions being executed by the processor to cause the computer device to perform the method of the first aspect described above.

In the embodiment of the application, a plurality of tasks for collecting map information can be obtained, the tasks are clustered according to the task characteristics of each task to obtain a plurality of task clusters, then a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters are determined, the target movement rule diagram is used for indicating the path distribution of the target user in N first areas in the map information, the task distribution rule diagram is used for indicating the task distribution in N second areas in the map information, further, the target task cluster corresponding to the target user is determined from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster, and the target task clusters are distributed to the target user, so that the target user executes each task in the target task clusters. By the method, the plurality of tasks can be flexibly divided according to the characteristics of the map information acquisition task, so that the tasks which are relatively close in time and space are divided into one task cluster, and the tasks are reasonably distributed to the corresponding users by combining with the movement rule of the users, so that the acquisition efficiency can be effectively improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a task allocation system according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of a task allocation method according to an embodiment of the present application;

fig. 3a is a schematic structural diagram of a target movement rule diagram and a task distribution rule diagram according to an embodiment of the present application;

fig. 3b is a schematic structural diagram of another target movement rule diagram and task distribution rule diagram according to an embodiment of the present application;

FIG. 3c is a schematic diagram of a target movement rule diagram and a task distribution rule diagram according to another embodiment of the present application;

FIG. 4 is a flowchart of another task allocation method according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of a clustering process according to an embodiment of the present application;

FIG. 6a is a flowchart of another task allocation method according to an embodiment of the present application;

FIG. 6b is a flowchart illustrating a task allocation method according to another embodiment of the present application;

FIG. 6c is a schematic flow chart of another clustering process according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a task allocation device according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.

The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Machine Learning (ML) is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

The scheme provided by the embodiment of the application relates to the technology of artificial intelligence such as machine learning, and the like, and is specifically described by the following embodiments:

at present, most of the schemes for collecting mass crowd-sourced information take a fixed number of vehicles as research objects, for example, aiming at the task of collecting air quality information, a taxi driver can be given corresponding rewards so as to drive the taxi driver to drive to a data collection sparse area as much as possible to collect the air quality information. It can be seen that the existing scheme mainly uses a taxi driver as a research object, although the taxi driver has strong mobility, the taxi driver cannot guarantee to cover all tasks to be collected for profit, the space-time distribution of air quality has strong regularity, meanwhile, the distribution of data requirements is relatively fixed, the difference between the distribution and map information collection is great, the buildings and the layout in cities are increasingly changed, and the update of map information is also not fixed, so that the distribution process of map information crowdsourcing collection tasks cannot be optimized by using the existing distribution method.

In view of the above problems, an embodiment of the present application provides a task allocation method, which may acquire a plurality of tasks for collecting map information, perform clustering processing on the plurality of tasks according to task features of each task to obtain a plurality of task clusters, determine a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, further determine a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster, and allocate the target task cluster to the target user, so that the target user executes each task in the target task cluster. By implementing the method, a plurality of tasks can be flexibly divided according to the distribution rule of the tasks, the tasks which are relatively close in time and space are divided into a task cluster, and the tasks are reasonably distributed to the corresponding users by combining with the movement rule of the users, so that the acquisition efficiency can be effectively improved.

It should be noted that, the task allocation method provided in the present embodiment may be specifically applied to a task allocation system, please refer to fig. 1, and fig. 1 is a schematic diagram of a task allocation system according to an embodiment of the present application. The present application relates to a terminal 101 and a server 102.

Taking the terminal 101 as an example, the terminal 101 acquires an acquisition task request initiated by a target user through the terminal 101, and sends the acquisition task request to the server 102. The server 102 determines a target movement law map of the target user according to the historical route set of the target user included in the acquisition task request. The server 102 acquires a plurality of tasks for collecting map information, clusters the plurality of tasks according to task characteristics of each task to obtain a plurality of task clusters, the server 102 determines a task distribution rule diagram of each task cluster in the plurality of task clusters, and determines a target task cluster corresponding to a target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster.

Subsequently, the server 102 sends the determined target task cluster to the terminal 101, and the terminal 101 displays the target task cluster, so that the user executes each task in the target task cluster.

The terminal 101 shown in fig. 1 may be an intelligent device such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a mobile internet device (MID, mobile Internet Device), a wearable device, and the like. The terminal 101 and the server 102 may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein.

Referring to fig. 2, fig. 2 is a flow chart of a task allocation method according to an embodiment of the application. The method is applied to a computer device, and can be executed by the computer device, wherein the computer device can be a server, and as shown in fig. 2, the task allocation method can include:

s201: acquiring a plurality of tasks for collecting map information, and clustering the plurality of tasks according to task characteristics of each task to obtain a plurality of task clusters.

In one implementation, a plurality of tasks may be acquired, where the tasks may be tasks that acquire map information, where the map information may be map information within a region, for example, a city or a town, etc., and the tasks that acquire map information may be acquiring information related to a city layout, such as a building or a river, in the city, where the tasks that acquire map information may be acquiring a geographic location or a building height or a floor area of building a in the city. After the server acquires a plurality of tasks for collecting map information, the plurality of tasks can be clustered according to the task characteristics of each task to obtain a plurality of task clusters.

The task features of each task may include a trigger time feature and a trigger space feature, where the trigger time feature may refer to a time when the task is to be executed, the trigger space feature may be a geographic location where the task is located, the geographic location may be GPS information, and the GPS information may be represented by (x, y).

In one implementation, the server may determine coordinates of each task in a space-time coordinate system according to the trigger time feature and the trigger space feature of each task, where the space-time coordinate system is used to represent the time feature and the space feature of different tasks. The space-time coordinate system may specifically be a three-dimensional coordinate system in a mathematical definition, the time feature may be a time to be executed by the task described above, and the space feature may be a geographic location where the task described above is located. For example, the time characteristic of triggering task a among the plurality of tasks is t1, the spatial characteristic of triggering is (x 1, y 1), the coordinates of task a in the space-time coordinate system are (x 1, y1, t 1), and for example, the time characteristic of triggering task B among the plurality of tasks is t2, the spatial characteristic of triggering is (x 2, y 2), the coordinates of task B in the space-time coordinate system are (x 2, y2, t 2). Then, after the server determines the coordinates of each task in the space-time coordinate system, the server may divide the plurality of tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task, so as to implement clustering processing on the plurality of tasks. Wherein the coordinate distance may be a euclidean distance between two coordinates.

S202: and determining a target movement rule diagram of the target user and a task distribution rule diagram of each task cluster in the plurality of task clusters.

In one implementation manner, the server may determine a task distribution rule diagram of each task cluster in the plurality of task clusters according to task characteristics of the tasks in each task cluster, specifically, for any task cluster, a position of each task in one diagram may be determined according to a trigger time characteristic and a trigger space characteristic of each task in any task cluster, where the diagram may be referred to as a task distribution rule diagram, a small circle may be used to represent one task in the task distribution rule diagram, or may be used to represent one task in other manners, where the application is not limited, and an image marked by 31 in fig. 3a is a task distribution rule diagram corresponding to any task cluster, where each small circle in the image represents one task. The task distribution rule diagram may be used to indicate task distribution in different areas in the map information, where in order to distinguish task distribution in different areas, the task distribution rule diagram may be divided into N areas, and each area may be referred to as a first area, i.e. the task distribution rule diagram may be divided into N first areas. Wherein, N is a positive integer, and the size of N can be preset or set according to the requirement, and the application is not limited. Optionally, when dividing the task distribution rule diagram, the task distribution rule diagram may be divided into N identical first areas, or N different first areas, which is not limited in the present application. While the shape of the first area may be square or rectangular or polygonal, etc., and is not limited in the present application. For example, each small box in the image labeled 31 in fig. 3a may represent a first region, each first region in the task distribution law map is identical, and the shape of the first region is square.

In one implementation, a server may obtain a set of target historical routes for a target user, wherein the set of target historical routes includes one or more target historical routes. Then, the server may determine a target movement law map for the target user based on one or more target historical routes in the set of target historical routes. Specifically, each target historical route in the target historical route set can be displayed on a graph, and then the graph can be called a target movement rule graph, for example, an image marked by 32 in fig. 3a is a target movement rule graph of a target user, each line in the image represents one target historical route, and the target movement rule graph can be used for indicating the path distribution of the target user in different areas in map information. In order to distinguish path distribution in different areas, the target movement rule diagram may be divided into N areas, each area may be referred to as a second area, i.e., the task distribution rule diagram may be divided into N second areas. The size and shape of the second region may be understood as the same as the first region. For example, each small box in the image labeled 32 in FIG. 3a may represent a second region, each second region in the task distribution pattern being identical, and the shape of the second region being square.

It should be noted that, the division of the target movement rule diagram of the target user and the task distribution rule diagram area of each task cluster is the same processing, that is, each first area in the target movement rule diagram and each second area in the task distribution rule diagram of any task cluster are correspondingly equal in shape and size. In a specific implementation, the target movement rule diagram of the target user may divide the target movement rule diagram into N second regions corresponding to the N first regions in the task distribution rule diagram according to the manner of dividing the N first regions in the task distribution rule diagram, as shown in fig. 3B, which is a display schematic diagram corresponding to the N second regions in the target movement rule diagram and the N first regions in any task distribution rule diagram, the image marked by 33 in fig. 3B is any task distribution rule diagram, the image marked by 34 is the target movement rule diagram, as shown in fig. 3B, for the division of the target movement rule diagram and the regions in any task distribution rule diagram, the positions of the second regions and the first regions are correspondingly equal, for example, the positions of the first region A1 in the task distribution rule diagram and the second region B1 in the target movement rule diagram in the respective diagram are equal.

S203: and determining a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster.

In one implementation manner, the server may determine, from a plurality of task clusters, a target task cluster corresponding to the target user according to the target movement rule diagram and the task distribution rule diagram of each task cluster. In a specific implementation, the server may determine a matrix corresponding to the target movement rule diagram, and a matrix corresponding to the task distribution rule diagram of each task cluster, where the matrix corresponding to the target movement rule diagram may be referred to as a first matrix, and the matrix corresponding to the task distribution rule diagram of each task cluster may be referred to as a second matrix, i.e. the task distribution rule diagram of each task cluster corresponds to a second matrix. Then, after determining the first matrix and the plurality of second matrices, the server may determine a distance between the first matrix and each of the plurality of second matrices, and screen out a second matrix having a distance from the first matrix smaller than a preset distance, and may call the second matrix having a distance from the first matrix smaller than the preset distance as the target second matrix. After determining the second matrix target, the server may acquire a task distribution rule diagram corresponding to the target second matrix, and may call the task distribution rule diagram corresponding to the target second matrix as a target task distribution rule diagram, and further, the server may determine a task cluster corresponding to the target task distribution rule diagram as a target task cluster corresponding to the target user.

For example, the server may determine the distance between the first matrix and each of the plurality of second matrices, assuming 7 second matrices (E1, E2, E3, E4, E5, E6, E7 for each of the 7 second matrices, and T1, T2, T3, T4, T5, T6, T7 for each of the 7 second matrices) and the distances between the first matrix determined by the server and each of the 7 second matrices are D1, D2, D3, D4, D5, D6, D7, respectively. If the preset distance is D, the server may compare the distance with the preset distance as D, and screen out values smaller than D from D1, D2, D3, D4, D5, D6, and D7, and if both D3 and D4 are smaller than D, it may determine that the second matrix corresponding to D3 is E3 and the second matrix corresponding to D4 is E4, where the second matrix is a task distribution rule diagram corresponding to E3 and is T3, the second matrix is a task distribution rule diagram corresponding to E4 and is T4, and further, the server may determine that the task distribution rule diagram is a task cluster corresponding to T3 and the task cluster corresponding to T4 as a target task cluster corresponding to the target user.

In one implementation manner, a specific implementation manner of determining the first matrix corresponding to the target movement rule diagram may be: the server determines the occurrence frequency of the target user in each first area according to the path distribution in N first areas in the target movement rule diagram, and constructs a first matrix according to the N first areas and the occurrence frequency of the paths in each first area. In a specific implementation, the server may count the number of historical routes of the target user in each first area in the target movement rule chart, where the number of historical routes of the target user in each first area in the target movement rule chart counted by the server is the occurrence frequency of the target user in each first area. The server may count the number of the historical routes of the target user in the N first areas, and may refer to the number of the historical routes of the target user in any one first area as the first numerical value, that is, the server may count the number of the historical routes of the target user in any one first area to obtain the N first numerical values. After determining the N first values, the server may determine the first matrix according to the N first areas and the N first values corresponding to the N first areas respectively. The first matrix comprises N first numerical values, and the position of each first numerical value in the first matrix is determined by the position of the corresponding first area in the target movement rule diagram.

For example, assuming that a target movement rule diagram of N (n=9) first areas is shown as an image marked by 35 in fig. 3c, each square in the image marked by 35 represents one first area, it can be seen that the target movement rule diagram includes 9 first areas, and the first value corresponding to each first area obtained by the statistics of the server is P ₁ ,P ₂ ,…,P ₉ Then, the first matrix H corresponding to the target movement rule diagram may be expressed as h= [ P ] ₁ ,P ₂ ,P ₃ ；P ₄ ,P ₅ ,P ₆ ；P ₇ ,P ₈ ,P ₉ ]。

In one implementation manner, the reference second matrix corresponding to the reference task distribution rule diagram of any reference task cluster in each task cluster, that is, the specific implementation manner of determining the reference second matrix in the plurality of second matrices may be: the server obtains the number of tasks in each second area according to task distribution in N second areas in the reference task distribution rule diagram, and builds a reference second matrix according to the N second areas and the number of tasks in each second area. In a specific implementation, the server may count the number of tasks in each second area in the reference task distribution rule diagram. The server may count the number of tasks in the N second areas, and may refer to the number of tasks in any one of the second areas as the second value, that is, the server may count the number of tasks in the N second areas. After determining the N second values, the server may determine the reference second matrix according to the N second areas and the N second values corresponding to the N second areas, respectively. The reference second matrix comprises N second numerical values, and the position of each second numerical value in the reference second matrix is determined by the position of the corresponding second area in the reference task distribution regular graph.

For example, assuming that a reference task distribution rule diagram of N (n=9) second areas is shown as an image marked by 36 in fig. 3c, and each square in the image marked by 36 represents one second area, it can be seen that the reference task distribution rule diagram includes 9 second areas, and the second value corresponding to each second area obtained by the statistics of the server is R ₁ ,R ₂ ,…,R ₉ Then, the second reference matrix M corresponding to the reference task distribution rule diagram may be expressed as m= [ R ₁ ,R ₂ ,R ₃ ；R ₄ ,R ₅ ,R ₆ ；R ₇ ,R ₈ ,R ₉ ]。

In one implementation, the specific implementation of determining the distance between the first matrix and each of the plurality of second matrices may be: the server performs normalization processing on the first matrix and the plurality of second matrices to obtain a normalized first target matrix and a plurality of normalized second target matrices, and after determining the first target matrix and the plurality of second target matrices, performs a difference operation on the first target matrix and each second target matrix to obtain a plurality of difference matrices, wherein the difference operation on the first target matrix and each second target matrix can obtain one difference matrix of the plurality of difference matrices. After determining to obtain a plurality of difference matrixes, performing modulo operation on each difference matrix, where the result obtained by performing modulo operation on each difference matrix is the distance between the corresponding first matrix and each second matrix. The modulo operation on the difference matrix may refer to the following equation 1.

Wherein C _F Representing the result obtained by the modulo C operation of the difference matrix, C _ij Representing the individual elements in the difference matrix C.

For example, consider the case of determining the distance between the first matrix and the reference second matrix, and assume that the first matrix is h= [ P ] ₁ ,P ₂ ,P ₃ ；P ₄ ,P ₅ ,P ₆ ；P ₇ ,P ₈ ,P ₉ ]The reference second matrix is m= [ R ₁ ,R ₂ ,R ₃ ；R ₄ ,R ₅ ,R ₆ ；R ₇ ,R ₈ ,R ₉ ]The server may normalize H and M to obtain a first target matrix H ₁ ＝[P ₁₁ ,P ₂₁ ,P ₃₁ ；P ₄₁ ,P ₅₁ ,P ₆₁ ；P ₇₁ ,P ₈₁ ,P ₉₁ ]A second target matrix M ₁ ＝[R ₁₁ ,R ₂₁ ,R ₃₁ ；R ₄₁ ,R ₅₁ ,R ₆₁ ；R ₇₁ ,R ₈₁ ,R ₉₁ ]Then the first target matrix H ₁ And a second target matrix M ₁ Performing a difference operation to obtain a difference matrix L= [ P ] ₁₁ -R ₁₁ ,P ₂₁ -R ₂₁ ,P ₃₁ -R ₃₁ ；P ₄₁ -R ₄₁ ,P ₅₁ -R ₅₁ ,P ₆₁ -R ₆₁ ；P ₇₁ -R ₇₁ ,P ₈₁ -R ₈₁ ,P ₉₁ -R ₉₁ ](or described as a difference matrix l= [ L ] ₁ ,L ₂ ,L ₃ ；L ₄ ,L ₅ ,L ₆ ；L ₇ ,L ₈ ,L ₉ ]Wherein L is ₁ ＝P ₁₁ -R ₁₁ The other values in the difference matrix L are analogized in turn and are not described in detail here). After determining the difference matrix L, the difference matrix L may be subjected to a modulo operation, and the result obtained by performing the modulo operation on the difference matrix L is (L ₁ ² +L ₂ ² +L ₃ ² +L ₄ ² +L ₅ ² +L ₆ ² +L ₇ ² +L ₈ ² +L ₉ ² ) ^1/2 The result is the distance between the first matrix H and the reference second matrix M.

S204: and distributing the target task cluster to the target user so that the target user executes each task in the target task cluster.

In one implementation, after determining a target task cluster of a target user, the server may allocate the target task cluster to the target user, and after the target user obtains the target task cluster obtained by the server, each task in the target task cluster may be executed.

As can be seen from the above, if the method of rewarding mechanism is adopted to drive the collector to complete the task of collecting the map information, when the collector is recruited to perform the task, a certain amount of subsidy and rewards are required to be given according to the cost required for completing the task, and the monetary cost is generated in the process. Through reasonable task clustering processing and distribution, the total empty driving distance of all tasks can be as low as possible, and the monetary cost given to the collection personnel can be sufficiently reduced. The distance of empty driving refers to the distance that the collection personnel needs to travel to the collection site before starting the collection task. In addition, in the task allocation process, the acquisition tasks can be allocated according to the movement preference of the acquisition personnel, so that the positions of the acquisition tasks allocated by the acquisition personnel are the positions frequently passed by the acquisition personnel as much as possible, namely, the acquisition personnel are familiar with the routes of the areas where the acquisition tasks are located, and the acquisition efficiency can be improved and the acquisition cost can be reduced.

In the embodiment of the application, a plurality of tasks for collecting map information can be obtained, the tasks are clustered according to the task characteristics of each task to obtain a plurality of task clusters, the server can also determine a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, and further, the server can determine a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster, and allocate the target task cluster to the target user so that the target user executes each task in the target task cluster. By implementing the method, a plurality of tasks can be flexibly divided according to the distribution rule of the tasks, the tasks which are relatively close in time and space are divided into a task cluster, and the tasks are reasonably distributed to the corresponding users by combining with the movement rule of the users, so that the acquisition efficiency can be effectively improved, and the time can be saved.

Referring to fig. 4, fig. 4 is a flowchart illustrating another task allocation method according to an embodiment of the present application. The method is applied to a computer device, and can be executed by the computer device, wherein the computer device can be a server, and as shown in fig. 4, the task allocation method can include:

s401: a plurality of tasks are acquired for the acquisition of map information.

S402: and determining the coordinates of each task in the space-time coordinate system according to the triggering time characteristics and the triggering space characteristics of each task.

The specific implementation of steps S401 and S402 may be referred to the specific description of step S201 in the above embodiment, and will not be repeated here.

S403: the plurality of tasks are divided into a plurality of task clusters according to coordinate distances between coordinates of each task.

In one implementation, after the server determines the coordinates of each task in the space-time coordinate system, the server may divide the plurality of tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task, so as to implement clustering processing on the plurality of tasks. Alternatively, the coordinate distance may be a euclidean distance between two coordinates.

In one implementation, the server may perform clustering on the plurality of tasks according to the first clustering distance to obtain at least one first task cluster. The coordinate distance between the coordinates of each task in each first task cluster and the first central coordinate is smaller than the first clustering distance, and the first central coordinate can be any one of the coordinates corresponding to the plurality of tasks respectively. The first cluster distance may be preset. After the server obtains at least one first task cluster through clustering, the task number corresponding to the tasks included in each first task cluster can be obtained, so that whether the clustering needs to be performed again on the first task cluster in the at least one first task cluster is determined according to the task number. In a specific implementation, the server may compare the number of tasks corresponding to the tasks included in each first task cluster with a preset number, where the preset number is preset.

If the number of tasks greater than or equal to the preset number exists, a first task cluster corresponding to the number of tasks greater than or equal to the preset number can be determined, and the first task cluster corresponding to the number of tasks greater than or equal to the preset number can be called as a candidate first task cluster. Then the candidate first task cluster may be added to the set of candidate task clusters. Wherein, a plurality of task clusters divided by a plurality of tasks can be determined according to the candidate task cluster set.

If the number of tasks is smaller than the preset number, a first task cluster corresponding to the number of tasks smaller than the preset number can be determined, the first task cluster corresponding to the number of tasks smaller than the preset number can be called a reference first task cluster, and the reference first task cluster is a first task cluster capable of being subjected to clustering again.

In one implementation, the clustering process may be performed again on the reference first task cluster in the above description, and the clustering process may be performed on the reference first task cluster according to the second cluster distance, so as to obtain at least one second task cluster. For example, 4 task clusters (a cluster marked by 51, a cluster marked by 52, a cluster marked by 53, and a cluster marked by 54) as shown in fig. 5 are referred to as a first task cluster, and after 4 task clusters are clustered according to a second cluster distance, the cluster marked by 51 and the cluster marked by 52 may be combined into one cluster marked by 55, the cluster marked by 53 and the cluster marked by 54 may be combined into one cluster marked by 56, and the cluster marked by 55 and the cluster marked by 56 are the second task cluster. Wherein the coordinate distance between the coordinates of the respective tasks in each second task cluster and the second center coordinate is smaller than the second aggregate distance, and the second center coordinate may be determined according to the coordinates of the respective tasks in each second task cluster, specifically, the coordinates of each second task cluster may be determined first, where the coordinates of any second task cluster are the average value of the coordinates of the respective tasks in any second task cluster, then, after determining the coordinates of each second task cluster, the coordinates in any second task cluster may be determined as the second center coordinate, for example, if there are 5 tasks in any second task cluster, and the coordinates of the 5 tasks are (x 1, y1, t 1), (x 2, y2, t 2), (x 3, y3, t 3), (x 4, y4, t 4) and (x 5, y5, t 5), then the coordinates of any second task cluster are ((x1+x2+x3+x4)/x 5+y 1+y2+y 3+t 5)/y 4+t 5+1+y 2+t 5. After obtaining at least one second task cluster, the number of tasks corresponding to the tasks included in each second task cluster may be compared with a preset number.

And if the number of the tasks in each second task cluster is greater than or equal to the preset number, determining to add each second task cluster into the candidate task cluster set. Then, the server may determine the task clusters included in the candidate task cluster set as the plurality of divided task clusters. If the second task clusters with the task number smaller than the preset number still exist in each second task cluster, the second task clusters with the task number smaller than the preset number can be called as reference second task clusters, and the server can also perform clustering processing on the reference second task clusters according to the third class distance so as to obtain at least one third task cluster. Wherein the coordinate distance between the coordinates of each task in each third task cluster and the third center coordinate is less than the third class distance. And sequentially cycling until the number of tasks in each task cluster is greater than or equal to the preset number. The method for determining the third center coordinate may refer to the above method for determining the second center coordinate, which is not described herein.

It should be noted that, the first clustering distance is smaller than the second clustering distance, and the second clustering distance is smaller than the third clustering distance, that is, the clustering distance utilized in the clustering process gradually increases with the increase of the clustering times, so that a plurality of task clusters with a smaller number of tasks can be ensured to be clustered into one task cluster again.

In one implementation manner, clustering a plurality of tasks with a first clustering distance to obtain a specific implementation manner of any target first task cluster in at least one first task cluster may be: the server determines a reference task from a plurality of tasks, and specifically, the server may randomly select one task from the plurality of tasks as the reference task, where the first center coordinate described above may be understood as the coordinate of the reference task. After determining the reference task, it may be determined from the plurality of tasks according to the first clustering distance that the coordinate distance between the plurality of tasks and the coordinates of the reference task is smaller than the first clustering distance, and the task with the coordinate distance between the plurality of tasks and the coordinates of the reference task being smaller than the first clustering distance may be referred to as a related task. Then, after determining the plurality of associated tasks, the reference task and the plurality of associated tasks may be grouped into a class, where the reference task and the plurality of associated tasks are the first target task cluster.

The clustering processing is performed on the reference first task cluster according to the second cluster distance to obtain at least one second task cluster, and the clustering processing is performed on the reference second task cluster according to the third cluster distance to obtain at least one third task cluster, so that the clustering processing may be performed on a plurality of tasks according to the first cluster distance to obtain the target first task cluster, which is not described herein.

As can be seen from the foregoing, the embodiment of the present application can cope with the complexity of the task in space-time distribution by using the clustering method, can flexibly divide a plurality of tasks into a plurality of task clusters according to the distribution rule of the task itself, divide the task that is relatively close in time and space into one task cluster, and can cope with the space-time distribution and the proximity of the task in different scales by using the multi-clustering method, so that a plurality of tasks can be reasonably divided, so that the distribution of the subsequent tasks is more reasonable, thereby providing the acquisition efficiency.

S404: and determining a target movement rule diagram of the target user and a task distribution rule diagram of each task cluster in the plurality of task clusters.

S405: and determining a target task cluster corresponding to the target user from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster.

S406: and distributing the target task cluster to the target user so that the target user executes each task in the target task cluster.

The specific implementation of steps S404-S406 can be seen from the specific description of steps S202-S204 in the above embodiment, and will not be repeated here.

In the embodiment of the application, a plurality of tasks for collecting map information can be obtained, the coordinates of each task in a space-time coordinate system are determined according to the triggering time characteristics and the triggering space characteristics of each task, the plurality of tasks are divided into a plurality of task clusters according to the coordinate distance between the coordinates of each task, the target movement rule diagram of a target user and the task distribution rule diagram of each task cluster in the plurality of task clusters are determined, then the target task cluster corresponding to the target user is determined from the plurality of task clusters according to the target movement rule diagram and the task distribution rule diagram of each task cluster, and the target task clusters are distributed to the target user, so that the target user executes each task in the target task clusters. By the method, the characteristics of map information acquisition tasks in time and space distribution can be fully utilized, tasks which are close in time and space are divided into one task cluster, a plurality of tasks can be flexibly divided according to the distribution rule of the tasks, and the tasks are reasonably distributed to corresponding users by combining with the movement rule of the users, so that the acquisition efficiency can be effectively improved, and the time can be saved.

Fig. 6a is a schematic flow chart of another task allocation method according to an embodiment of the present application. In the flow shown in fig. 6a, the task allocation method may be divided into two branches, where one branch is to acquire multiple tasks for collecting map information from a task pool, and then perform hierarchical space-time clustering on the multiple tasks to obtain multiple task clusters. The hierarchical space-time clustering process is to perform multiple clustering processes on multiple tasks by using coordinates of each task in a space-time coordinate system in the above description, so that the number of tasks in each task cluster is greater than or equal to a preset number. The other branch is to acquire historical route data of the user and mine out a moving mode of the user according to the historical route data of the user. For any user, the historical route data of the user may be a historical route set of the target user in the description, and the moving mode of the user mined according to the historical route data of the user may be the target moving rule diagram of the target user. Further, according to the movement mode of the users, task clusters allocated to each user are determined.

Fig. 6b is a schematic flow chart of another task allocation method according to an embodiment of the present application. In the flow shown in fig. 6b, the task allocation method may be divided into two modules, one module is a hierarchical spatio-temporal clustering module, and the function of the module is mainly to perform clustering processing on a plurality of tasks so as to obtain a plurality of task clusters; the other module is a personalized allocation module based on mobile preference, and the function of the module reasonably allocates the plurality of task clusters obtained by the first module to users.

In one implementation, in the hierarchical spatio-temporal clustering module, multiple tasks may be packaged according to the spatio-temporal distribution of the collected tasks, that is, the tasks are clustered according to the trigger time features and the trigger space features of the tasks. Therefore, tasks with adjacent relations in time and space can be gathered into one type, the tasks are packaged, and then the packaged tasks are distributed to a small number of acquisition personnel, so that the acquisition personnel can execute the corresponding tasks, and if the distribution is unreasonable, the acquisition personnel may need to walk a long idle driving distance to traverse all the tasks in one task cluster. Or tasks with adjacent relations in time and space are respectively distributed to different collecting staff, more collecting staff may be needed, and especially in the case of fewer collecting staff, tasks cannot be reasonably distributed to the collecting staff, so that the efficiency of processing tasks is lower. The clustering mode adopted by the application is a hierarchical density clustering mode, namely a multi-clustering process is performed, and two dimensions are considered during clustering, namely a space dimension (which can be understood as the triggering space feature described above) and a time dimension (which can be understood as the triggering time feature described above). A specific process of the hierarchical spatio-temporal clustering process may include the following steps s11-s13, where each task may be understood as a coordinate point of each task in a space-time coordinate system, and each task may be understood as a point in the following description, that is, a plurality of tasks correspond to each point.

s11: a first neighborhood radius and density threshold is set, an initial point is determined from a plurality of points to be a current point, and the current point is placed in a current cluster.

In one implementation, a first neighborhood radius and a density threshold may be preset, where the neighborhood radius is the first cluster distance, and the density threshold is the preset number.

s12: a point having a distance from the current point smaller than the first neighborhood radius is determined from the plurality of points, and the current point and the point having a distance from the current point smaller than the first neighborhood radius are regarded as one cluster (which may be understood as the first task cluster described above). For example, taking the point a in fig. 6c as the current point, after determining the current point, a point having a distance from the point a smaller than the radius of the first neighborhood may be searched, and as seen in fig. 6c, one point a is a cluster. After determining a cluster, a point is selected from the points which do not become a cluster as a current point, and then a point with a distance smaller than the first neighborhood radius from the point B is searched, wherein 2 points are found in the cluster obtained by taking the point B as the current point, and the two points are the cluster, as shown in fig. 6 c. Then, a point is selected from the points which do not become clusters as the current point, the point C in FIG. 6C can be the current point, then the point with the distance smaller than the radius of the first neighborhood from the point C is searched, and as seen in FIG. 6C, 5 points are in the clusters obtained by taking the point C as the current point, and the 5 points are one cluster. And sequentially circulating.

s13: if all points already have a corresponding cluster, the clustering is ended, otherwise step s12 is looped. In the layering process, clustering can be performed by using larger and larger clustering distances. After clustering all the points to obtain a plurality of first task clusters, the first task clusters with fewer points in the first task clusters can be clustered by using larger clustering distances, and clustering is performed again by using larger clustering distances with reference to the first task clusters, and the specific process can comprise the following steps s11-s13:

and s31, setting a second neighborhood radius, and performing clustering according to the second neighborhood radius, wherein the second neighborhood radius is larger than the first neighborhood radius, and the second neighborhood radius is the second clustering distance.

And s32, determining the coordinates of each reference first task cluster, and re-clustering the plurality of reference first task clusters according to the coordinates of each reference first task cluster and the second neighborhood radius.

In one implementation, the determining of the coordinates of any reference first task cluster may be: the coordinates of each point in any reference first task cluster are obtained, and then the average value of the coordinates is taken as the coordinates of any reference first task cluster.

s33, terminating when the number of the points in the task cluster obtained by clustering reaches the density threshold, otherwise, circularly executing step s32.

After the above hierarchical spatiotemporal clustering, as shown in fig. 6b, a plurality of points corresponding to a plurality of tasks may be divided into a plurality of clusters, and the region marked by 61 in fig. 6b is one cluster.

In one implementation, the mobile preference-based personalized allocation module performs more rational task allocation in combination with the mobile preferences of the user and the spatial distribution of the tasks in each task cluster. The module marked 62 in fig. 6b may be a process of matching each acquisition person with each task cluster, wherein the image marked 63 in fig. 6b may be a movement law thermodynamic diagram of the acquisition person, and the image marked 64 may be a task cluster. After matching, each acquisition person can be distributed to the corresponding task cluster so that the acquisition person executes each task in the task cluster. The specific implementation process of the matching can be as follows: and determining the distance between the mobile hotspot matrix (the first matrix) of the acquisition personnel and the task hotspot matrix (the second matrix) of the task space distribution in the task cluster, and taking the distance between the mobile hotspot matrix and the task hotspot matrix as a judgment standard for judging whether the acquisition personnel are matched with the task cluster. Specifically, the distance may be compared with a preset distance, and if the distance is smaller than the preset distance, the task cluster may be allocated to the collection personnel. The matching between the acquisition personnel and the task cluster can be solved by a bipartite graph matching method and a greedy algorithm, and other modes can be used, so that the method is not limited by the application.

In one implementation, the determination of the mobile hotspot matrix of the collection personnel may be determined according to the mobile preference of the collection personnel, and the specific implementation process may be: firstly, acquiring historical routes of the acquisition personnel, and obtaining a movement law thermodynamic diagram of the acquisition personnel through statistics of the number of the historical routes in different areas of a map, wherein the movement law thermodynamic diagram can be an image marked by 65 in fig. 6b, the movement law thermodynamic diagram can be divided into a plurality of areas, each area has corresponding brightness values, each brightness value is the number of the historical routes in the corresponding area, then the brightness values corresponding to each area in the movement law thermodynamic diagram are normalized, and each brightness value in the normalized movement law thermodynamic diagram can be constructed into a movement hotspot matrix.

In one implementation, the specific implementation process of determining the task hotspot matrix may be: for a task cluster, a task distribution thermodynamic diagram of the task cluster can be obtained through statistics of the number of tasks in different areas of a map, wherein the task distribution thermodynamic diagram can be divided into a plurality of areas, each area has corresponding brightness values, each brightness value is the number of tasks in the corresponding area, then the brightness values corresponding to each area in the task distribution thermodynamic diagram are normalized, and each brightness value in the normalized task distribution thermodynamic diagram can be constructed into a task hotspot matrix.

In one implementation, to verify the functional characteristics of the present solution, the following experiments are performed with the record logs of the map information collection tasks recorded in 1 st year 2000 and before in a certain area, and 29 collection personnel in the log, 2414 map information collection tasks, and the packing records and completion conditions of the manpower packing and distribution currently used. To demonstrate the performance of the present scheme, the present scheme is compared to a meshed spatiotemporal partition packing scheme. As can be seen from Table 1, compared with the meshing space-time division packing scheme, the empty driving distance of the scheme is smaller than that of the meshing space-time division packing scheme on the evaluation index of the empty driving distance, and the improvement can be approximately 50%, which indicates that the scheme can greatly improve the efficiency of completing the acquisition task and greatly reduce the cost.

Table 1:

scheme for the production of a semiconductor device	Distance of empty	Lifting up
			Gridding space-time division packing scheme	6683m	46.45％
The proposal is that	3579m	-

Referring to fig. 7, fig. 7 is a schematic structural diagram of a task-based distribution device according to an embodiment of the present application. The task allocation device described in the present embodiment includes:

a clustering unit 701, configured to obtain a plurality of tasks for collecting map information, and perform clustering processing on the plurality of tasks according to task features of each task, so as to obtain a plurality of task clusters;

A first determining unit 702, configured to determine a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, where the target movement rule diagram is used to indicate path distribution of the target user in N first areas in the map information, and the task distribution rule diagram is used to indicate task distribution in the N second areas in the map information, where N is a positive integer;

a second determining unit 703, configured to determine, from the plurality of task clusters, a target task cluster corresponding to the target user according to the target movement rule diagram and the task distribution rule diagram of each task cluster;

and the allocation unit 704 is configured to allocate the target task cluster to the target user, so that the target user executes each task in the target task cluster.

In one implementation, the task features include a trigger time feature and a trigger space feature, and the clustering unit 701 is specifically configured to:

determining coordinates of each task in a space-time coordinate system according to the triggering time characteristics and the triggering space characteristics of each task, wherein the space-time coordinate system is used for representing the time characteristics and the space characteristics of different tasks;

And dividing the tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task so as to realize the clustering processing of the tasks.

In one implementation, the clustering unit 701 is specifically configured to:

clustering the plurality of tasks by a first clustering distance to obtain at least one first task cluster, wherein the coordinate distance between the coordinates of each task in each first task cluster and the first center coordinate is smaller than the first clustering distance;

acquiring the task number corresponding to the tasks included in each first task cluster;

if the number of the tasks is smaller than the preset number of the reference first task clusters, adding candidate first task clusters with the number of the tasks larger than or equal to the preset number in each first task cluster into a candidate task cluster set, and clustering the reference first task clusters by a second cluster distance to obtain at least one second task cluster, wherein the coordinate distance between the coordinates of each task in each second task cluster and the second center coordinate is smaller than the second cluster distance;

when the number of the tasks in each second task cluster is greater than or equal to the preset number, determining to add each second task cluster to the candidate task cluster set;

And determining the task clusters included in the candidate task cluster set as a plurality of divided task clusters.

In one implementation, the clustering unit 701 is specifically configured to:

determining a reference task from the plurality of tasks;

determining a plurality of associated tasks corresponding to the reference task from the plurality of tasks according to a first clustering distance, wherein the coordinate distance between the coordinate of each associated task and the coordinate of the reference task is smaller than the first clustering distance;

and gathering the reference task and the plurality of related tasks into one type to obtain a first target task cluster, wherein the coordinate of the reference task is a first center coordinate in the first target task cluster.

In one implementation, the second determining unit 703 is specifically configured to:

determining a first matrix corresponding to the target movement rule diagram and a second matrix corresponding to the task distribution rule diagram of each task cluster, so as to obtain a plurality of second matrices;

determining a distance between the first matrix and each of the plurality of second matrices;

screening a target second matrix with a distance smaller than a preset distance from the first matrix, and obtaining a target task distribution rule diagram corresponding to the target second matrix;

And determining the task cluster corresponding to the target task distribution rule diagram as the target task cluster corresponding to the target user.

determining occurrence frequencies of the target user in each first area according to path distribution in N first areas in the target movement rule diagram, and constructing a first matrix according to the N first areas and the occurrence frequencies of paths in each first area, wherein the first matrix comprises N first numerical values, each first numerical value corresponds to the occurrence frequencies in each first area, and the position of each first numerical value in the first matrix is determined by the position of the corresponding first area in the target movement rule diagram;

acquiring the number of tasks in each second area according to task distribution in N second areas in the reference task distribution regular graph, and constructing a reference second matrix according to the N second areas and the number of tasks in each second area, wherein the reference second matrix comprises N second numerical values, each second numerical value corresponds to the number of tasks in each second area, and the position of each second numerical value in the reference second matrix is determined by the position of the corresponding second area in the reference task distribution regular graph.

normalizing the first matrix and the plurality of second matrices to obtain a first target matrix and a plurality of second target matrices;

performing difference operation on the first target matrix and each second target matrix to obtain a plurality of difference matrixes;

and performing modular operation on each difference matrix to obtain the distance between the first target matrix and each second target matrix.

It will be appreciated that the division of the units in the embodiment of the present application is illustrative, and is merely a logic function division, and other division manners may be actually implemented. The functional units in the embodiment of the application can be integrated in one processing unit, or each unit can exist alone physically, or two or more units are integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

Referring to fig. 8, fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the application. The computer device described in this embodiment may be a server, and the computer device includes: a processor 801, a memory 802, and a network interface 803. Data may be interacted between the processor 801, the memory 802, and the network interface 803.

The processor 801 may be a central processing unit (Central Processing Unit, CPU) which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 802 may include read only memory and random access memory, and provides program instructions and data to the processor 801. A portion of memory 802 may also include non-volatile random access memory. Wherein the processor 801, when calling the program instructions, is configured to execute:

In one implementation, the task features include a trigger time feature and a trigger space feature, and the processor 801 is specifically configured to:

In one implementation, the processor 801 is specifically configured to:

determining a reference task from the plurality of tasks;

In one implementation, the processor 801 is specifically configured to:

The embodiment of the application also provides a computer storage medium, and the computer storage medium stores program instructions, and the program can include part or all of the steps of the task allocation method in the corresponding embodiment of fig. 2 or fig. 4 when being executed.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of action described, as some steps may be performed in other order or simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps performed in the embodiments of the methods described above.

The task allocation method, the task allocation device, the computer equipment and the storage medium provided by the embodiment of the application are described in detail, and specific examples are applied to the explanation of the principle and the implementation mode of the application, and the explanation of the above embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims

1. A method of task allocation, comprising:

determining a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, wherein the target movement rule diagram is used for indicating path distribution of the target user in N first areas in the map information, the task distribution rule diagram is used for indicating task distribution in N second areas in the map information, and each first area in the target movement rule diagram is corresponding to each second area in the task distribution rule diagram of any task cluster in shape and size, wherein N is a positive integer;

distributing the target task cluster to the target user so that the target user executes each task in the target task cluster;

the task features include a triggering time feature and a triggering space feature, and the clustering processing is performed on the plurality of tasks according to the task feature of each task to obtain a plurality of task clusters, including:

determining coordinates of each task in a space-time coordinate system according to the triggering time characteristics and the triggering space characteristics of each task, wherein the space-time coordinate system is used for representing the time characteristics and the space characteristics of different tasks; dividing the tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task so as to realize clustering processing of the tasks;

the determining, according to the target movement rule diagram and the task distribution rule diagram of each task cluster, a target task cluster corresponding to the target user from the plurality of task clusters includes:

Determining a first matrix corresponding to the target movement rule diagram and a second matrix corresponding to the task distribution rule diagram of each task cluster, so as to obtain a plurality of second matrices; the first matrix comprises N first numerical values, each first numerical value corresponds to the occurrence frequency of each target user in a first area, the position of each first numerical value in the first matrix is determined by the position of the corresponding first area in the target movement rule diagram, each second matrix comprises N second numerical values, each second numerical value corresponds to the number of tasks in each second area in the corresponding task distribution rule diagram, and the position of each second numerical value in the corresponding second matrix is determined by the position of the corresponding second area in the corresponding task distribution rule diagram;

determining a distance between the first matrix and each of the plurality of second matrices; and screening a target second matrix with the distance smaller than the preset distance from the first matrix from the plurality of second matrices, acquiring a target task distribution rule diagram corresponding to the target second matrix, and determining a task cluster corresponding to the target task distribution rule diagram as a target task cluster corresponding to the target user.

2. The method of claim 1, wherein the dividing the plurality of tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task comprises:

3. The method of claim 2, wherein clustering the plurality of tasks at a first cluster distance to obtain at least one first task cluster comprises:

determining a reference task from the plurality of tasks;

4. The method according to claim 1, wherein determining the first matrix corresponding to the target movement law map and the second matrix corresponding to the task distribution law map of each task cluster respectively includes:

determining the occurrence frequency of the target user in each first area according to the path distribution in N first areas in the target movement rule diagram, and constructing a first matrix according to the N first areas and the occurrence frequency of the paths in each first area;

According to task distribution in N second areas in each task distribution rule diagram, the number of tasks in each second area in each task distribution rule diagram is obtained, and a second matrix corresponding to each task distribution rule diagram is built according to the N second areas in each task distribution rule diagram and the number of tasks in each second area.

5. The method of claim 1, wherein the determining the distance between the first matrix and each of the plurality of second matrices comprises:

6. A task assigning apparatus, comprising:

The first determining unit is used for determining a target movement rule diagram of a target user and a task distribution rule diagram of each task cluster in the plurality of task clusters, wherein the target movement rule diagram is used for indicating path distribution of the target user in N first areas in the map information, the task distribution rule diagram is used for indicating task distribution in N second areas in the map information, and each first area in the target movement rule diagram is equal to each second area in the task distribution rule diagram of any task cluster in shape and size, wherein N is a positive integer;

an allocation unit, configured to allocate the target task cluster to the target user, so that the target user executes each task in the target task cluster;

the task features comprise trigger time features and trigger space features, and the clustering unit is used for determining coordinates of each task in a space-time coordinate system according to the trigger time features and the trigger space features of each task, wherein the space-time coordinate system is used for representing the time features and the space features of different tasks; dividing the tasks into a plurality of task clusters according to the coordinate distance between the coordinates of each task so as to realize clustering processing of the tasks;

The second determining unit is configured to determine a first matrix corresponding to the target movement rule diagram and a second matrix corresponding to the task distribution rule diagram of each task cluster, so as to obtain a plurality of second matrices; the first matrix comprises N first numerical values, each first numerical value corresponds to the occurrence frequency of each target user in a first area, the position of each first numerical value in the first matrix is determined by the position of the corresponding first area in the target movement rule diagram, each second matrix comprises N second numerical values, each second numerical value corresponds to the number of tasks in each second area in the corresponding task distribution rule diagram, and the position of each second numerical value in the corresponding second matrix is determined by the position of the corresponding second area in the corresponding task distribution rule diagram; determining a distance between the first matrix and each of the plurality of second matrices; and screening a target second matrix with the distance smaller than the preset distance from the first matrix from the plurality of second matrices, acquiring a target task distribution rule diagram corresponding to the target second matrix, and determining a task cluster corresponding to the target task distribution rule diagram as a target task cluster corresponding to the target user.

7. A computer device comprising a processor, a memory and a network interface, the processor, memory and network interface being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.

8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-5.